use anyhow::Result;
use rayon::prelude::*;
use serde::{Deserialize, Serialize};
use std::sync::Arc;
use crate::cache::prediction_cache::PredictionCache;
use crate::compute::memory::memory_pool::MemoryPool;

#[derive(Debug, Deserialize)]
pub struct PredictionRequest {
    pub stock_code: String,
    pub model_type: String,
    pub timeframe: String,
    pub features: Vec<f64>,
}

#[derive(Debug, Serialize)]
pub struct PredictionResult {
    pub stock_code: String,
    pub model_type: String,
    pub predictions: Vec<f64>,
    pub confidence: f64,
    pub from_cache: bool,
}

pub struct BatchPredictor {
    cache: Arc<PredictionCache>,
    memory_pool: MemoryPool,
}

impl BatchPredictor {
    pub fn new(cache: Arc<PredictionCache>) -> Self {
        Self {
            cache,
            memory_pool: MemoryPool::new(),
        }
    }

    pub fn predict(&self, requests: Vec<PredictionRequest>) -> Result<Vec<PredictionResult>> {
        // 使用rayon进行并行处理
        requests.par_iter()
            .map(|request| {
                let cache_key = format!("{}:{}", request.stock_code, request.timeframe);
                
                // 尝试从缓存获取结果
                if let Some(cached) = self.cache.get(&cache_key) {
                    return Ok(PredictionResult {
                        stock_code: request.stock_code.clone(),
                        model_type: request.model_type.clone(),
                        predictions: cached.predictions,
                        confidence: cached.confidence,
                        from_cache: true,
                    });
                }
                
                // 如果没有缓存，执行预测
                let (predictions, confidence) = self.predict_single(request)?;
                
                // 缓存结果
                self.cache.set(
                    &cache_key,
                    predictions.clone(),
                    confidence,
                    &request.timeframe,
                );
                
                Ok(PredictionResult {
                    stock_code: request.stock_code.clone(),
                    model_type: request.model_type.clone(),
                    predictions,
                    confidence,
                    from_cache: false,
                })
            })
            .collect()
    }

    fn predict_single(&self, request: &PredictionRequest) -> Result<(Vec<f64>, f64)> {
        // 这里应该实现具体的预测逻辑
        // 目前返回一个示例实现
        let mut predictions = Vec::new();
        let features = &request.features;
        
        // 简单的移动平均预测示例
        let window_size = 5;
        for i in 0..5 {  // 预测未来5个时间点
            let start = if i < window_size { 0 } else { i - window_size };
            let avg = features[start..=i].iter().sum::<f64>() / (i - start + 1) as f64;
            predictions.push(avg);
        }
        
        // 计算简单的置信度
        let confidence = 0.8;  // 这里应该基于模型输出计算实际的置信度
        
        Ok((predictions, confidence))
    }
}